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Agents-A1-4B-Fable-Preview-GGUF
hotdogs/Agents-A1-4B-Fable-Preview-GGUF
Agents-A1-4B-Fable-Preview-GGUF is a image-text-to-text model from hotdogs. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as agpl-3.0.
<h1 align="center"🤖 Agents-A1-4B-Fable-Preview-GGUF</h1
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At a glance
- Task
- Image-Text-to-Text
- Library
- gguf
- License
- agpl-3.0
- Access
- Public
- Created
- Jul 26, 2026
- Updated
- Aug 2, 2026
- SHA
- 0bd8d5b1
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Base models
Datasets
Model card
From the Hugging Face model README
<h1 align="center">🤖 Agents-A1-4B-Fable-Preview-GGUF</h1>
<p align="center">
<b>GGUF Quantized — 4B Vision-Language Agent Model · Fable Reasoning · Tool-Calling</b>
</p>
<p align="center">
<img src="https://img.shields.io/badge/license-AGPL--3.0-red">
<img src="https://img.shields.io/badge/Base-hotdogs/Agents--A1--4B--Fable--Preview-blue">
<img src="https://img.shields.io/badge/GGUF-Q4_K_M-brightgreen">
<img src="https://img.shields.io/badge/Vision-✅-brightgreen">
<img src="https://img.shields.io/badge/Fable-Reasoning-orange">
</p>
<br>
GGUF quantized version of hotdogs/Agents-A1-4B-Fable-Preview — optimized for llama.cpp inference with vision support.
Evaluation
SWE-bench Verified (subset)
| Metric | Value |
|---|---|
| Resolve rate | 49.5% (99/200) |
| Instances evaluated | 200 / 500 (first 200, index-ordered slice) |
| Scaffold | mini-swe-agent v2.4.6 |
| Agent config | step_limit=250, cost_limit=$3.0, temperature=0.0 |
| Inference | llama.cpp, F16, ctx=131072 |
| Empty patches | 13/200 (6.5%) |
| Date | 2026-08-02 |
Note: This is evaluated on a 200-instance subset (first 200 by dataset index, not a stratified random sample), not the full 500-instance SWE-bench Verified set. Results may differ from a full-set evaluation. Full results and prediction files available at [link if you publish them].
✨ Key Features
| Capability | Description |
|---|---|
| 🖼️ Vision Understanding | Image-text-to-text with mmproj |
| 🧠 Fable Reasoning | Step-by-step CoT with <think> blocks |
| 🔧 Tool Calling | llama.cpp --tools all support |
| 💬 Multi-turn | Trained on full agent trajectories |
| 🌏 Thai + English | Native bilingual support |
| 💻 Code & Shell | Python, bash, system tasks |
| ⚡ Fast Inference | IQ4_NL fits in ~3 GB VRAM |
📦 Downloads
| File | Size | Description |
|---|---|---|
Agents-A1-4B-Fable-IQ4_NL.gguf | 2.61 GB | Recommended — best quality/speed balance for 8GB VRAM |
Agents-A1-4B-Fable-Q4_K_M_imatrix.gguf | 2.71 GB | Q4_K_M + imatrix — slightly higher quality |
Agents-A1-4B-Fable-Q6_K_imatrix.gguf | 3.46 GB | Q6_K + imatrix — higher quality, more VRAM |
Agents-A1-4B-Fable-Q8_0_imatrix.gguf | 4.48 GB | Q8_0 + imatrix — almost lossless |
Agents-A1-4B-Fable-f16.gguf | 8.42 GB | Full BF16 precision |
Agents-A1-4B-mmproj.gguf | 672 MB | Vision projector for image understanding |
imatrix.dat | 3.63 MB | Importance matrix data |
🎯 IQ4_NL is recommended for 8GB VRAM users — fits comfortably even at 128K context with flash-attention.
🚀 Usage
Docker (Recommended)
sudo docker run --rm -p 8080:8080 \
-v /root/models/:/models \
--gpus all \
--ulimit memlock=-1:-1 \
--env CUDA_VISIBLE_DEVICES=0 \
ghcr.io/ggml-org/llama.cpp:full-cuda --server \
-m /models/Agents-A1-4B-Fable-IQ4_NL.gguf \
--mmproj /models/Agents-A1-4B-mmproj.gguf \
--host 0.0.0.0 --port 8080 \
--n-gpu-layers 999 \
--ctx-size 131072 \
--batch-size 4096 \
--ubatch-size 256 \
--cache-type-k f16 \
--cache-type-v f16 \
--flash-attn on \
--cont-batching \
--mlock \
--temp 0.95 \
--top-k 40 \
--top-p 0.9 \
--min-p 0.0 \
-n -1 \
--no-mmap \
--parallel 1 --tools all \
--dry-multiplier 0.05 \
--jinja --dry-sequence-breaker none \
--repeat-penalty 1.1
Parameter Explanation
| Parameter | Purpose |
|---|---|
--mmproj | Vision projector for image understanding |
--ctx-size 131072 | 128K context window |
--flash-attn on | Flash attention for speed |
--cache-type-k/v f16 | BF16 KV cache for quality |
--cont-batching | Continuous batching for multi-turn |
--tools all | Enable tool/function calling |
--jinja | Use Jinja2 chat template |
--mlock | Lock memory for performance |
llama.cpp (Direct)
# Quick text-only test
./llama-cli -m Agents-A1-4B-Fable-IQ4_NL.gguf \
-p "Hello" -n 100 --temp 0.6 -ngl 999
# Vision inference
./llama-cli -m Agents-A1-4B-Fable-IQ4_NL.gguf \
--mmproj Agents-A1-4B-mmproj.gguf \
--image photo.jpg \
-p "What is in this image?" -n 256 --temp 0.6 -ngl 999
🧬 Model Information
This is a GGUF quantized version of hotdogs/Agents-A1-4B-Fable-Preview, which is a fine-tune of InternScience/Agents-A1-4B.
| Parameter | Value |
|---|---|
| Base Model | hotdogs/Agents-A1-4B-Fable-Preview |
| Parameters | ~4.29B |
| Architecture | Qwen3.5 hybrid (Linear + Full attention) |
| Vision | ✅ 24-layer ViT encoder via mmproj |
| Context | Up to 128K tokens |
| Format | ChatML (Jinja2 template) |
| Fine-tuning | Fable-style reasoning traces (3,500 samples, 3 epochs) |
🙏 Acknowledgements / ขอบคุณ
- InternScience — For the Agents-A1-4B base model and mmproj vision projector 🙏
- mmproj source — Extracted from InternScience/Agents-A1-4B-Q4_K_M-GGUF
- Qwen Team (Alibaba) — For the Qwen3.5 architecture
- Unsloth AI — For training optimizations
- All dataset contributors and the open-source AI community ❤️
💖 Support / โปรดสนับสนุน
If you find this model useful, please consider supporting my work!
หากคุณคิดว่าโมเดลนี้มีประโยชน์ กรุณาสนับสนุนผลงานของฉันด้วยนะคะ! 🙏
₿ Bitcoin — BTC:
bc1qf27cyk3vmugcdyv9xdtuv5jwz37863crpj5c9v
Thank you for your support! 🙏✨
ขอบคุณมากๆ สำหรับการสนับสนุนค่า! 💖🤗
Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert
- Task
- Image-Text-to-Text
- Library
- gguf
- License
- agpl-3.0
- Languages
- en, th
- Created
- Jul 26, 2026
- Updated
- Aug 2, 2026